def get_other_results(queries, qml_rankings, num_ranks=None):
document_lookup = read_cache('./doc_lookup.json', get_robust_documents)
document_title_to_id = read_cache('./document_title_to_id.json',
lambda: print('failed'))
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [
document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids
]
tokenizer = Tokenizer(
rules=[handle_caps, fix_html, spec_add_spaces, rm_useless_spaces])
tokenized_documents = read_cache('tok_docs.json',
lambda: tokenizer.process_all(documents))
tokenized_queries = tokenizer.process_all(queries)
bm25 = BM25(tokenized_documents)
average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)
bm25_rankings = []
glove_rankings = []
rm3_rankings = []
glove = get_glove_lookup(embedding_dim=300, use_large_embed=True)
docs_lms = _calc_docs_lms(bm25.df, bm25.f)
for q, qml_ranking in progressbar(zip(tokenized_queries, qml_rankings)):
bm25_rankings.append(
_get_bm25_ranking(bm25, qml_ranking, q, average_idf=average_idf))
glove_rankings.append(
_get_glove_ranking(glove, tokenized_documents, qml_ranking, q))
rm3_rankings.append(_get_rm3_ranking(docs_lms, bm25.f, qml_ranking, q))
return bm25_rankings, glove_rankings, rm3_rankings
def baselines_eval():
rankings_to_eval = read_query_test_rankings()
qrels = parse_qrels()
query_ids = list(qrels.keys())
query_lookup = get_robust_eval_queries()
queries = [query_lookup[query_id] for query_id in query_ids]
k = 10 if len(sys.argv) == 1 else int(sys.argv[1])
document_lookup = read_cache(name('./doc_lookup.json', ['with_titles']),
get_robust_documents_with_titles)
document_title_to_id = read_cache('./document_title_to_id.json',
lambda: print('failed'))
ordered_rankings_to_eval = [[
document_title_to_id[title] for title in rankings_to_eval[query]
] for query in query_ids]
ordered_qrels = [[document_title_to_id[title] for title in qrels[query]]
for query in query_ids]
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [
document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids
]
tokenizer = Tokenizer(
rules=[handle_caps, fix_html, spec_add_spaces, rm_useless_spaces])
tokenized_documents = read_cache(
'tok_docs.json',
lambda: tokenizer.process_all(clean_documents(documents)))
tokenized_queries = tokenizer.process_all(clean_documents(queries))
bm25 = gensim_bm25.BM25(tokenized_documents)
# with open('./caches/106756_most_common_doc.json', 'r') as fh:
# doc_token_set = set(json.load(fh))
# corpus, token_lookup = tokens_to_indexes(tokenized_documents,
# None,
# token_set=doc_token_set)
# corpus = [[[token_lookup[term], f] for term, f in doc_fs.items()] for doc_fs in bm25.f]
# tfidf = TfidfModel(corpus)
# lsi = LsiModel(tfidf, id2word=_.invert(token_lookup), num_topics=300)
glove_rankings = []
# lsi_rankings = []
glove = get_glove_lookup(embedding_dim=300, use_large_embed=True)
encoded_docs = torch.stack(
[encode_glove_fs(glove, bm25.idf, doc_fs) for doc_fs in bm25.f])
encoded_docs = encoded_docs / torch.norm(encoded_docs, dim=1).unsqueeze(1)
for q, qml_ranking in progressbar(zip(tokenized_queries,
ordered_rankings_to_eval),
max_value=len(tokenized_queries)):
doc_ids = qml_ranking[:k] if '--rerank' in sys.argv else None
glove_rankings.append(
rank_glove(glove, bm25.idf, encoded_docs, q, doc_ids=doc_ids))
# lsi_rankings.append(rank_lsi(lsi, tfidf, [token_lookup[term] if term in token_lookup else 0 for term in q], doc_ids=doc_ids))
print('indri:', metrics_at_k(ordered_rankings_to_eval, ordered_qrels, k))
print('glove:', metrics_at_k(glove_rankings, ordered_qrels, k))
def main():
document_lookup = read_cache('./doc_lookup.json', get_robust_documents)
document_title_to_id = create_id_lookup(document_lookup.keys())
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids]
tokenizer = Tokenizer()
tokenized_documents = read_cache('tok_docs.json',
lambda: tokenizer.process_all(documents))
bm25 = BM25(tokenized_documents)
with open('./doc_word_idf.json', 'w+') as fh:
json.dump(bm25.idf, fh)
def get_texts(texts, with_preprocess=False, pre_rules=None):
r"""
Uses fastai's Tokenizer because it does a series of very convenients things
during the tokenization process
See here: https://docs.fast.ai/text.transform.html#Tokenizer
"""
tok_func = Tokenizer()
if with_preprocess:
texts = [" ".join(simple_preprocess(s)) for s in texts]
if pre_rules:
tok_func.pre_rules = pre_rules + tok_func.pre_rules
tokens = tok_func.process_all(texts)
return tokens
def preprocess_texts(texts,
token_lookup=None,
num_tokens=None,
token_set=None,
drop_if_any_unk=False):
tokenizer = Tokenizer()
tokenized = tokenizer.process_all(texts)
idx_texts, token_lookup = tokens_to_indexes(
tokenized,
token_lookup,
num_tokens=num_tokens,
token_set=token_set,
drop_if_any_unk=drop_if_any_unk)
return idx_texts, token_lookup
def main():
global model_to_save
global experiment
global rabbit
rabbit = MyRabbit(args)
if rabbit.model_params.dont_limit_num_uniq_tokens:
raise NotImplementedError()
if rabbit.model_params.frame_as_qa: raise NotImplementedError
if rabbit.run_params.drop_val_loss_calc: raise NotImplementedError
if rabbit.run_params.use_softrank_influence and not rabbit.run_params.freeze_all_but_last_for_influence:
raise NotImplementedError
if rabbit.train_params.weight_influence: raise NotImplementedError
experiment = Experiment(rabbit.train_params + rabbit.model_params +
rabbit.run_params)
print('Model name:', experiment.model_name)
use_pretrained_doc_encoder = rabbit.model_params.use_pretrained_doc_encoder
use_pointwise_loss = rabbit.train_params.use_pointwise_loss
query_token_embed_len = rabbit.model_params.query_token_embed_len
document_token_embed_len = rabbit.model_params.document_token_embed_len
_names = []
if not rabbit.model_params.dont_include_titles:
_names.append('with_titles')
if rabbit.train_params.num_doc_tokens_to_consider != -1:
_names.append('num_doc_toks_' +
str(rabbit.train_params.num_doc_tokens_to_consider))
if not rabbit.run_params.just_caches:
if rabbit.model_params.dont_include_titles:
document_lookup = read_cache(name('./doc_lookup.json', _names),
get_robust_documents)
else:
document_lookup = read_cache(name('./doc_lookup.json', _names),
get_robust_documents_with_titles)
num_doc_tokens_to_consider = rabbit.train_params.num_doc_tokens_to_consider
document_title_to_id = read_cache(
'./document_title_to_id.json',
lambda: create_id_lookup(document_lookup.keys()))
with open('./caches/106756_most_common_doc.json', 'r') as fh:
doc_token_set = set(json.load(fh))
tokenizer = Tokenizer()
tokenized = set(
sum(
tokenizer.process_all(list(
get_robust_eval_queries().values())), []))
doc_token_set = doc_token_set.union(tokenized)
use_bow_model = not any([
rabbit.model_params[attr] for attr in
['use_doc_out', 'use_cnn', 'use_lstm', 'use_pretrained_doc_encoder']
])
use_bow_model = use_bow_model and not rabbit.model_params.dont_use_bow
if use_bow_model:
documents, document_token_lookup = read_cache(
name(f'./docs_fs_tokens_limit_uniq_toks_qrels_and_106756.pkl',
_names),
lambda: prepare_fs(document_lookup,
document_title_to_id,
num_tokens=num_doc_tokens_to_consider,
token_set=doc_token_set))
if rabbit.model_params.keep_top_uniq_terms is not None:
documents = [
dict(
nlargest(rabbit.model_params.keep_top_uniq_terms,
_.to_pairs(doc), itemgetter(1)))
for doc in documents
]
else:
documents, document_token_lookup = read_cache(
name(
f'./parsed_docs_{num_doc_tokens_to_consider}_tokens_limit_uniq_toks_qrels_and_106756.json',
_names), lambda: prepare(document_lookup,
document_title_to_id,
num_tokens=num_doc_tokens_to_consider,
token_set=doc_token_set))
if not rabbit.run_params.just_caches:
train_query_lookup = read_cache('./robust_train_queries.json',
get_robust_train_queries)
train_query_name_to_id = read_cache(
'./train_query_name_to_id.json',
lambda: create_id_lookup(train_query_lookup.keys()))
train_queries, query_token_lookup = read_cache(
'./parsed_robust_queries_dict.json',
lambda: prepare(train_query_lookup,
train_query_name_to_id,
token_lookup=document_token_lookup,
token_set=doc_token_set,
drop_if_any_unk=True))
query_tok_to_doc_tok = {
idx: document_token_lookup.get(query_token)
or document_token_lookup['<unk>']
for query_token, idx in query_token_lookup.items()
}
names = [RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set]]
if rabbit.train_params.use_pointwise_loss or not rabbit.run_params.just_caches:
train_data = read_cache(
name('./robust_train_query_results_tokens_qrels_and_106756.json',
names), lambda: read_query_result(
train_query_name_to_id,
document_title_to_id,
train_queries,
path='./indri/query_result' + RANKER_NAME_TO_SUFFIX[
rabbit.train_params.ranking_set]))
else:
train_data = []
q_embed_len = rabbit.model_params.query_token_embed_len
doc_embed_len = rabbit.model_params.document_token_embed_len
if rabbit.model_params.append_difference or rabbit.model_params.append_hadamard:
assert q_embed_len == doc_embed_len, 'Must use same size doc and query embeds when appending diff or hadamard'
if q_embed_len == doc_embed_len:
glove_lookup = get_glove_lookup(
embedding_dim=q_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
q_glove_lookup = glove_lookup
doc_glove_lookup = glove_lookup
else:
q_glove_lookup = get_glove_lookup(
embedding_dim=q_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
doc_glove_lookup = get_glove_lookup(
embedding_dim=doc_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
num_query_tokens = len(query_token_lookup)
num_doc_tokens = len(document_token_lookup)
doc_encoder = None
if use_pretrained_doc_encoder or rabbit.model_params.use_doc_out:
doc_encoder, document_token_embeds = get_doc_encoder_and_embeddings(
document_token_lookup, rabbit.model_params.only_use_last_out)
if rabbit.model_params.use_glove:
query_token_embeds_init = init_embedding(q_glove_lookup,
query_token_lookup,
num_query_tokens,
query_token_embed_len)
else:
query_token_embeds_init = from_doc_to_query_embeds(
document_token_embeds, document_token_lookup,
query_token_lookup)
if not rabbit.train_params.dont_freeze_pretrained_doc_encoder:
dont_update(doc_encoder)
if rabbit.model_params.use_doc_out:
doc_encoder = None
else:
document_token_embeds = init_embedding(doc_glove_lookup,
document_token_lookup,
num_doc_tokens,
document_token_embed_len)
if rabbit.model_params.use_single_word_embed_set:
query_token_embeds_init = document_token_embeds
else:
query_token_embeds_init = init_embedding(q_glove_lookup,
query_token_lookup,
num_query_tokens,
query_token_embed_len)
if not rabbit.train_params.dont_freeze_word_embeds:
dont_update(document_token_embeds)
dont_update(query_token_embeds_init)
else:
do_update(document_token_embeds)
do_update(query_token_embeds_init)
if rabbit.train_params.add_rel_score:
query_token_embeds, additive = get_additive_regularized_embeds(
query_token_embeds_init)
rel_score = RelScore(query_token_embeds, document_token_embeds,
rabbit.model_params, rabbit.train_params)
else:
query_token_embeds = query_token_embeds_init
additive = None
rel_score = None
eval_query_lookup = get_robust_eval_queries()
eval_query_name_document_title_rels = get_robust_rels()
test_query_names = []
val_query_names = []
for query_name in eval_query_lookup:
if len(val_query_names) >= 50: test_query_names.append(query_name)
else: val_query_names.append(query_name)
test_query_name_document_title_rels = _.pick(
eval_query_name_document_title_rels, test_query_names)
test_query_lookup = _.pick(eval_query_lookup, test_query_names)
test_query_name_to_id = create_id_lookup(test_query_lookup.keys())
test_queries, __ = prepare(test_query_lookup,
test_query_name_to_id,
token_lookup=query_token_lookup)
eval_ranking_candidates = read_query_test_rankings(
'./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
test_candidates_data = read_query_result(
test_query_name_to_id,
document_title_to_id,
dict(zip(range(len(test_queries)), test_queries)),
path='./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
test_ranking_candidates = process_raw_candidates(test_query_name_to_id,
test_queries,
document_title_to_id,
test_query_names,
eval_ranking_candidates)
test_data = process_rels(test_query_name_document_title_rels,
document_title_to_id, test_query_name_to_id,
test_queries)
val_query_name_document_title_rels = _.pick(
eval_query_name_document_title_rels, val_query_names)
val_query_lookup = _.pick(eval_query_lookup, val_query_names)
val_query_name_to_id = create_id_lookup(val_query_lookup.keys())
val_queries, __ = prepare(val_query_lookup,
val_query_name_to_id,
token_lookup=query_token_lookup)
val_candidates_data = read_query_result(
val_query_name_to_id,
document_title_to_id,
dict(zip(range(len(val_queries)), val_queries)),
path='./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
val_ranking_candidates = process_raw_candidates(val_query_name_to_id,
val_queries,
document_title_to_id,
val_query_names,
eval_ranking_candidates)
val_data = process_rels(val_query_name_document_title_rels,
document_title_to_id, val_query_name_to_id,
val_queries)
train_normalized_score_lookup = read_cache(
name('./train_normalized_score_lookup.pkl', names),
lambda: get_normalized_score_lookup(train_data))
test_normalized_score_lookup = get_normalized_score_lookup(
test_candidates_data)
val_normalized_score_lookup = get_normalized_score_lookup(
val_candidates_data)
if use_pointwise_loss:
normalized_train_data = read_cache(
name('./normalized_train_query_data_qrels_and_106756.json', names),
lambda: normalize_scores_query_wise(train_data))
collate_fn = lambda samples: collate_query_samples(
samples,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
train_dl = build_query_dataloader(
documents,
normalized_train_data,
rabbit.train_params,
rabbit.model_params,
cache=name('train_ranking_qrels_and_106756.json', names),
limit=10,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=False)
test_dl = build_query_dataloader(
documents,
test_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_dl = build_query_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
model = PointwiseScorer(query_token_embeds, document_token_embeds,
doc_encoder, rabbit.model_params,
rabbit.train_params)
else:
if rabbit.train_params.use_noise_aware_loss:
ranker_query_str_to_rankings = get_ranker_query_str_to_rankings(
train_query_name_to_id,
document_title_to_id,
train_queries,
limit=rabbit.train_params.num_snorkel_train_queries)
query_names = reduce(
lambda acc, query_to_ranking: acc.intersection(
set(query_to_ranking.keys()))
if len(acc) != 0 else set(query_to_ranking.keys()),
ranker_query_str_to_rankings.values(), set())
all_ranked_lists_by_ranker = _.map_values(
ranker_query_str_to_rankings, lambda query_to_ranking:
[query_to_ranking[query] for query in query_names])
ranker_query_str_to_pairwise_bins = get_ranker_query_str_to_pairwise_bins(
train_query_name_to_id,
document_title_to_id,
train_queries,
limit=rabbit.train_params.num_train_queries)
snorkeller = Snorkeller(ranker_query_str_to_pairwise_bins)
snorkeller.train(all_ranked_lists_by_ranker)
calc_marginals = snorkeller.calc_marginals
else:
calc_marginals = None
collate_fn = lambda samples: collate_query_pairwise_samples(
samples,
use_bow_model=use_bow_model,
calc_marginals=calc_marginals,
use_dense=rabbit.model_params.use_dense)
if rabbit.run_params.load_influences:
try:
with open(rabbit.run_params.influences_path) as fh:
pairs_to_flip = defaultdict(set)
for pair, influence in json.load(fh):
if rabbit.train_params.use_pointwise_loss:
condition = True
else:
condition = influence < rabbit.train_params.influence_thresh
if condition:
query = tuple(pair[1])
pairs_to_flip[query].add(tuple(pair[0]))
except FileNotFoundError:
pairs_to_flip = None
else:
pairs_to_flip = None
train_dl = build_query_pairwise_dataloader(
documents,
train_data,
rabbit.train_params,
rabbit.model_params,
pairs_to_flip=pairs_to_flip,
cache=name('train_ranking_qrels_and_106756.json', names),
limit=10,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=False)
test_dl = build_query_pairwise_dataloader(
documents,
test_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_dl = build_query_pairwise_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_rel_dl = build_query_pairwise_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True,
rel_vs_irrel=True,
candidates=val_ranking_candidates,
num_to_rank=rabbit.run_params.num_to_rank)
model = PairwiseScorer(query_token_embeds,
document_token_embeds,
doc_encoder,
rabbit.model_params,
rabbit.train_params,
use_bow_model=use_bow_model)
train_ranking_dataset = RankingDataset(
documents,
train_dl.dataset.rankings,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
test_ranking_dataset = RankingDataset(
documents,
test_ranking_candidates,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
relevant=test_dl.dataset.rankings,
query_tok_to_doc_tok=query_tok_to_doc_tok,
cheat=rabbit.run_params.cheat,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
val_ranking_dataset = RankingDataset(
documents,
val_ranking_candidates,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
relevant=val_dl.dataset.rankings,
query_tok_to_doc_tok=query_tok_to_doc_tok,
cheat=rabbit.run_params.cheat,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
if rabbit.train_params.memorize_test:
train_dl = test_dl
train_ranking_dataset = test_ranking_dataset
model_data = DataBunch(train_dl,
val_rel_dl,
test_dl,
collate_fn=collate_fn,
device=torch.device('cuda') if
torch.cuda.is_available() else torch.device('cpu'))
multi_objective_model = MultiObjective(model, rabbit.train_params,
rel_score, additive)
model_to_save = multi_objective_model
if rabbit.train_params.memorize_test:
try:
del train_data
except:
pass
if not rabbit.run_params.just_caches:
del document_lookup
del train_query_lookup
del query_token_lookup
del document_token_lookup
del train_queries
try:
del glove_lookup
except UnboundLocalError:
del q_glove_lookup
del doc_glove_lookup
if rabbit.run_params.load_model:
try:
multi_objective_model.load_state_dict(
torch.load(rabbit.run_params.load_path))
except RuntimeError:
dp = nn.DataParallel(multi_objective_model)
dp.load_state_dict(torch.load(rabbit.run_params.load_path))
multi_objective_model = dp.module
else:
train_model(multi_objective_model, model_data, train_ranking_dataset,
val_ranking_dataset, test_ranking_dataset,
rabbit.train_params, rabbit.model_params,
rabbit.run_params, experiment)
if rabbit.train_params.fine_tune_on_val:
fine_tune_model_data = DataBunch(
val_rel_dl,
val_rel_dl,
test_dl,
collate_fn=collate_fn,
device=torch.device('cuda')
if torch.cuda.is_available() else torch.device('cpu'))
train_model(multi_objective_model,
fine_tune_model_data,
val_ranking_dataset,
val_ranking_dataset,
test_ranking_dataset,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
experiment,
load_path=rabbit.run_params.load_path)
multi_objective_model.eval()
device = model_data.device
gpu_multi_objective_model = multi_objective_model.to(device)
if rabbit.run_params.calc_influence:
if rabbit.run_params.freeze_all_but_last_for_influence:
last_layer_idx = _.find_last_index(
multi_objective_model.model.pointwise_scorer.layers,
lambda layer: isinstance(layer, nn.Linear))
to_last_layer = lambda x: gpu_multi_objective_model(
*x, to_idx=last_layer_idx)
last_layer = gpu_multi_objective_model.model.pointwise_scorer.layers[
last_layer_idx]
diff_wrt = [p for p in last_layer.parameters() if p.requires_grad]
else:
diff_wrt = None
test_hvps = calc_test_hvps(
multi_objective_model.loss,
gpu_multi_objective_model,
DeviceDataLoader(train_dl, device, collate_fn=collate_fn),
val_rel_dl,
rabbit.run_params,
diff_wrt=diff_wrt,
show_progress=True,
use_softrank_influence=rabbit.run_params.use_softrank_influence)
influences = []
if rabbit.train_params.use_pointwise_loss:
num_real_samples = len(train_dl.dataset)
else:
num_real_samples = train_dl.dataset._num_pos_pairs
if rabbit.run_params.freeze_all_but_last_for_influence:
_sampler = SequentialSamplerWithLimit(train_dl.dataset,
num_real_samples)
_batch_sampler = BatchSampler(_sampler,
rabbit.train_params.batch_size,
False)
_dl = DataLoader(train_dl.dataset,
batch_sampler=_batch_sampler,
collate_fn=collate_fn)
sequential_train_dl = DeviceDataLoader(_dl,
device,
collate_fn=collate_fn)
influences = calc_dataset_influence(gpu_multi_objective_model,
to_last_layer,
sequential_train_dl,
test_hvps,
sum_p=True).tolist()
else:
for i in progressbar(range(num_real_samples)):
train_sample = train_dl.dataset[i]
x, labels = to_device(collate_fn([train_sample]), device)
device_train_sample = (x, labels.squeeze())
influences.append(
calc_influence(multi_objective_model.loss,
gpu_multi_objective_model,
device_train_sample,
test_hvps,
diff_wrt=diff_wrt).sum().tolist())
with open(rabbit.run_params.influences_path, 'w+') as fh:
json.dump([[train_dl.dataset[idx][1], influence]
for idx, influence in enumerate(influences)], fh)